Geotechnical characterisation of coal spoil piles using high-resolution optical and multispectral data: A machine learning approach
Article
Article Title | Geotechnical characterisation of coal spoil piles using high-resolution optical and multispectral data: A machine learning approach |
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ERA Journal ID | 1722 |
Article Category | Article |
Authors | Thiruchittampalam, Sureka, Banerjee, Bikram Pratap, Glenn, Nancy F. and Raval, Simit |
Journal Title | Engineering Geology |
Journal Citation | 329 |
Article Number | 107406 |
Year | 2024 |
Publisher | Elsevier |
Place of Publication | Netherlands |
ISSN | 0013-7952 |
1872-6917 | |
Digital Object Identifier (DOI) | https://doi.org/10.1016/j.enggeo.2024.107406 |
Web Address (URL) | https://www.sciencedirect.com/science/article/pii/S0013795224000048 |
Abstract | Geotechnical characterisation of spoil piles has traditionally relied on the expertise of field specialists, which can be both hazardous and time-consuming. Although unmanned aerial vehicles (UAV) show promise as a remote sensing tool in various applications; accurately segmenting and classifying very high-resolution remote sensing images of heterogeneous terrains, such as mining spoil piles with irregular morphologies, presents significant challenges. The proposed method adopts a robust approach that combines morphology-based segmentation, as well as spectral, textural, structural, and statistical feature extraction techniques to overcome the difficulties associated with spoil pile characterisation. Additionally, it incorporates minimum redundancy maximum relevance (mRMR) based feature selection and machine learning-based classification. This automated characterisation will serve as a proactive tool for dump stability assessment, providing crucial data for improved stability models and contributing to a greener and more responsible mining industry. |
Keywords | Object-based image analysis; Morphology-based segmentation; Waste materials; Mine dump; High-resolution UAV images; Shear strength parameters |
ANZSRC Field of Research 2020 | 401304. Photogrammetry and remote sensing |
401905. Mining engineering | |
Byline Affiliations | University of New South Wales |
University of Moratuwa, Sri Lanka | |
School of Surveying and Built Environment | |
Boise State University, United States |
https://research.usq.edu.au/item/z3v00/geotechnical-characterisation-of-coal-spoil-piles-using-high-resolution-optical-and-multispectral-data-a-machine-learning-approach
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